stdlib-js/math-base-special-max

Double-precision floating-point positive infinity.

Test if a double-precision floating-point numeric value is NaN.

C APIs for registering a Node-API module exporting an interface for invoking a binary numerical function.

Load a manifest for compiling source files.

A linear congruential pseudorandom number generator (LCG) whose output is shuffled.

Uniformly distributed pseudorandom numbers between 0 and 1.

Wrap `require` in a try/catch block.

tap-producing test harness for node and browsers

Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use cases including unit tests, server side functional tests and browser tests. Built for scale

Minimal TAP output formatter.

Benchmark harness.

Test if a double-precision floating-point numeric value is positive zero.

Test if a double-precision floating-point numeric value is negative zero.

A 32-bit Mersenne Twister pseudorandom number generator.

Natural logarithm of the beta function.

Incomplete gamma function.

Inverse incomplete beta function.

Gamma distribution logarithm of probability density function (PDF).

Hypergeometric distribution probability mass function (PMF).

Incomplete beta function and its first derivative.

Gamma distribution probability density function (PDF).

Hypergeometric distribution cumulative distribution function (CDF).

Negative binomial distribution probability mass function (PMF).

Hypergeometric distribution quantile function.

Perform a one-way analysis of variance.

Return the maximum absolute value.

Geometric distribution quantile function.

Chi-squared distribution mode.

Natural logarithm of the probability mass function (PMF) for a negative binomial distribution.

Natural logarithm of the probability mass function (PMF) for a hypergeometric distribution.

F distribution probability density function (PDF).

Divide two complex numbers.

Moving Grubbs' test for outliers.

Perform a series of row interchanges on an input matrix.

Online regression via stochastic gradient descent (SGD).

Grubbs' test for outliers.

Base (i.e., lower-level) special math functions.

Compute a Pearson product-moment correlation test between paired samples.

Compute the inverse of a double-precision complex floating-point number.

Locally-weighted polynomial regression via the LOWESS algorithm.

Incrementally perform binary classification using stochastic gradient descent (SGD).

Adjust supplied p-values for multiple comparisons via a specified method.

Return the maximum value, ignoring NaN.

REPL presentation.